1. Field of the Invention
The present invention generally relates to a method for allocating work in capacity planning and, more particularly, to a method for accurate capacity planning which deals with parallel, unrelated tools that can process the same operations at different rates and with the preferences for the sequence in which those tools are selected to accommodate the workload.
2. Background Description
The capacity of a manufacturing line is characterized by the tool set that occupies the line. This tool set may represent a large capital investment ($1B for semiconductor fabrication) and depreciation burden. It typically consists of multiple generations of tools giving rise to a mix of different equipment with different yields, availabilities and speeds for completing particular operations. Also, engineers typically have an understanding of which tools are best suited to perform a particular operation, which tools are next best, and so on. The best tool may be the fastest or highest yielding. The second best may be an older, slower, less reliable tool. Depending on the manufacturing environment and life cycles of the products and tools, there may be as many as five or more different tools that can perform a given process step, each with its own distinct operating characteristics.
Broadly speaking, manufacturing capacity planning addresses three kinds of problems:
(1) deciding the number of tools necessary to produce a particular product mix and volume; PA1 (2) deciding what is the "optimal" product mix and volume to maximize the value of an existing tool set; and PA1 (3) deciding on what additional tools to acquire to add to an existing tool set.
In a simple manufacturing environment, addressing all three questions is relatively straightforward. For example, for the case of calculating the required number of tools when operations are not shared among tools, one can simply divide the time required per day to perform all the operations done by a certain type of tool by the time available per day for this type of tool to arrive at an estimate of the number of required tools. However, in the more complex manufacturing environments in which different tools can perform the same or similar sets of operations, generally at different rates, these decisions become much more difficult because of the different ways in which work can be allocated among different tools. The necessity of respecting the preferred order in which the machines are assigned work further increases the level of the complexity of the problem.
Typically, capacity planning problems are addressed by making use of some type of mathematical model of the manufacturing process. The model may take the form of a simple spreadsheet, a detailed discrete event simulation, or a mathematical program such as a linear or mixed integer program. W. J. Hopp and M. L. Spearman, Factory Physics: Foundations of Manufacturing Management, Irwin (1996), and E. A. Silver and R. Peterson, Decision Systems for Inventory Management and Production Planning, 2.sup.nd Ed., John Wiley & Sons (1985), provide simple examples of conventional capacity planning problems and how to analyze them. W. Chou and J. Everton, "Capacity Planning for Development Wafer Fab Expansion", Proc. of the 1997 7.sup.th Annual IEEE/SEMI Advanced Semiconductor Manufacturing Converence, pp. 17-22 (1996), describe the use of a discrete event simulation model in capacity planning. K. M. Bretthauer and M. J. Cote, "Nonlinear Programming for Multiperiod Capacity Planning in a Manufacturing System", European Journal of Operational Research, 96:1, pp. 167-179 (1997), and R. G. Kasilingam and C. Roze, "Formulations of the Capacity Planning Problem Considering Manufacturing Flexibility", International Journal of Systems Science, 27:10, pp. 1027-1031 (1996), describe mathematical programming models for capacity planning. L. M. Wein, "Capacity Allocation in Generalized Jackson Networks", Operations Research Letters, Vol. 8. pp. 143-146 (1980), describes a method for capacity planning based on a queuing network model that assumes, among other things, that all tools capable of performing a given operation are identical. R. C. Leachman and T. F. Carmon, "On Capacity Modeling for Production Planning with Alternative Machine Types", IIE Transactions, 24:4, pp. 62-72 (1992), discuss capacity modeling with alternate machine types, but limit their discussion to the case that processing times among such alternate machine types are identical or proportional across operations they can perform. None of the above addresses capacity planning problems in which work can be allocated to different tools, with varying ratios of process times from operation to operation and in which there exists a preferred order in which tools are used.